R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is the base for many R packages listed in https://cran.r-project.org/

This software is also referenced in ORMS.

References in zbMATH (referenced in 3045 articles , 6 standard articles )

Showing results 1 to 20 of 3045.
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  1. Abdi, Hervé; Beaton, Derek: Principal component and correspondence analyses using R (to appear) (2018)
  2. Agasisti, Tommaso; Ieva, Francesca; Paganoni, Anna Maria: Heterogeneity, school-effects and the north/south achievement gap in Italian secondary education: evidence from a three-level mixed model (2017)
  3. Arangala, Crista; Yokley, Karen A.: Exploring calculus. Labs and projects with Mathematica (2017)
  4. Arcagni, Alberto: On the decomposition by sources of the zenga 1984 point and synthetic inequality indexes (2017)
  5. Asar, Özgür; Ilk, Ozlem; Dag, Osman: Estimating box-Cox power transformation parameter via goodness-of-fit tests (2017)
  6. Ashley Petersen, Noah Simon, Daniela Witten: SCALPEL: Extracting Neurons from Calcium Imaging Data (2017) arXiv
  7. Audigier, Vincent; Husson, François; Josse, Julie: MIMCA: multiple imputation for categorical variables with multiple correspondence analysis (2017)
  8. Baumer, Benjamin S.; Kaplan, Daniel T.; Horton, Nicholas J.: Modern data science with R (2017)
  9. Beckerman, Andrew P.; Petchey, Owen L.: Getting started with R. An introduction for biologists. (2017)
  10. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  11. Bolstad, William M.; Curran, James M.: Introduction to Bayesian statistics (2017)
  12. Chakar, S.; Lebarbier, E.; Lévy-Leduc, C.; Robin, S.: A robust approach for estimating change-points in the mean of an $\operatornameAR(1)$ process (2017)
  13. Chaturvedi, Nimisha; de Menezes, Renée X.; Goeman, Jelle J.: A global $\times$ global test for testing associations between two large sets of variables (2017)
  14. Cipolli, William III; Hanson, Timothy: Computationally tractable approximate and smoothed polya trees (2017)
  15. Dehmer, Matthias (ed.); Shi, Yongtang (ed.); Emmert-Streib, Frank (ed.): Computational network analysis with R. Applications in biology, medicine and chemistry (2017)
  16. Escuín, David; Polo, Lorena; Ciprés, David: On the comparison of inventory replenishment policies with time-varying stochastic demand for the paper industry (2017)
  17. Fan, Jianqing; Yao, Qiwei: The elements of financial econometrics (2017)
  18. Fan, Yanan; de Micheaux, Pierre Lafaye; Penev, Spiridon; Salopek, Donna: Multivariate nonparametric test of independence (2017)
  19. Filipiak, Katarzyna; Klein, Daniel; Roy, Anuradha: A comparison of likelihood ratio tests and Rao’s score test for three separable covariance matrix structures (2017)
  20. Fuchs, Nicole; Pölz, Werner; Bathke, Arne C.: Confidence intervals for population means of partially paired observations (2017)

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Further publications can be found at: http://journal.r-project.org/